Data Scientist

As a Data Scientist, we are at the cutting edge of solving some of the fundamental business problems using advanced data methodologies, statistics and machine learning algorithms.

In the Model Risk Management team, defend the company against model failures and find new ways of making better decisions with models. Use our statistics, software engineering, and business expertise to drive the best outcomes in both Risk Management and the Enterprise. Investing in new skills, building better tools, and maintaining a network of trusted partners.

Responsibilities

- Partner cross-functionally with data scientists, quantitative analysts, business analysts, software engineers, and project managers to manage the risk and uncertainty inherent in statistical and machine learning models in order to lead the best decisions, not just avoid the worst ones.

- Build and validate statistical and machine learning models through all phases of development, from design through training, evaluation and implementation.

- Develop new ways of identifying weak spots in model predictions earlier and with more confidence than the best available methods.

- Assess, challenge, and at times defend state-of-the-art decision-making systems to internal and regulatory partners.

- Leverage a broad stack of technologies - Python, R, Conda, AWS, and more - to reveal the insights hidden within huge volumes of data.

- Build upon your existing machine learning and statistical toolset - both by learning new technologies and by building custom software tools for data exploration, model performance evaluation, and more.

- Communicate technical subject matter clearly and concisely to individuals from various backgrounds both verbally and through written communication; prepare presentations of complex technical concepts and research results to non-specialist audiences and senior management.

- Flex your interpersonal skills to translate the complexity of your work into tangible business goals, and challenge model developers to advance their modeling, data, and analytic capabilities.

Qualifications

- Degree in statistics, math, engineering, economics, econometrics, financial engineering, finance, or operations research with a quantitative emphasis preferred

- Atleast 2 years relevant work experience

- Experience in Python or R